def _model_fn(self):
"""Returns a model_fn."""
def _train_op_fn(loss):
"""Defines train op used in ranking head."""
update_ops = tf.compat.v1.get_collection(
tf.compat.v1.GraphKeys.UPDATE_OPS)
minimize_op = self._optimizer.minimize(
loss=loss, global_step=tf.compat.v1.train.get_global_step())
train_op = tf.group([update_ops, minimize_op])
return train_op
ranking_head = head.create_ranking_head(
loss_fn=losses.make_loss_fn(
self._hparams.get("loss"),
weights_feature_name=self._hparams.get(_LOSS_WEIGHT),
reduction=self._loss_reduction),
eval_metric_fns=self._eval_metric_fns(),
train_op_fn=_train_op_fn)
return model.make_groupwise_ranking_fn(
group_score_fn=self._group_score_fn,
group_size=1,
transform_fn=self._transform_fn,
ranking_head=ranking_head)
def setUp(self):
super(GroupwiseRankingEstimatorTest, self).setUp()
ops.reset_default_graph()
self._model_dir = test.get_temp_dir()
gfile.MakeDirs(self._model_dir)
model_fn = model.make_groupwise_ranking_fn(
_group_score_fn,
group_size=2,
transform_fn=feature.make_identity_transform_fn(['context', 'weight']),
ranking_head=head.create_ranking_head(
loss_fn=losses.make_loss_fn(
losses.RankingLossKey.PAIRWISE_HINGE_LOSS,
weights_feature_name='weight'),
optimizer=training.AdagradOptimizer(learning_rate=0.1)))
self._estimator = estimator.Estimator(model_fn, self._model_dir)